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grade.py
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#!/usr/bin/env python3
from __future__ import annotations
import json
import math
import os
import unittest
from collections.abc import Callable
import click
import numpy as np
import pandas as pd
from pytest import approx
from aibuilders_exam.grading.context import ApplicationContext
from aibuilders_exam.grading.engine import ModuleItem, Param, TestGroup, TestScenario, TestSuite
from aibuilders_exam.grading.loader import NotebookLoader
from aibuilders_exam.helpers import rich_time, validate_pos
unit_testing = unittest.TestCase()
THIS_DIR = os.path.dirname(os.path.abspath(__file__))
FUNNY_WORDS = [
"poboy", "dobby", "cuppy", "hobby", "pudgy", "cocky", "bawdy", "miffy", "foamy", "jazzy",
"caddy", "jaggy", "muddy", "jumpy", "duchy", "poppy", "yucky", "bumpy", "biddy", "choky",
"fuzzy", "buggy", "byway", "gaudy", "dizzy", "moody", "muggy", "gabby", "dicky", "doggy",
"wiggy", "hooky", "kiddy", "dumpy", "waddy", "guppy", "woody", "doozy", "giddy", "mummy",
"gamay", "piggy", "divvy", "gawky", "bobby", "woozy", "ducky", "buddy", "cooky", "mommy",
"dodgy", "middy", "gummy", "kooky", "jiffy", "humpy", "picky", "comfy", "puppy", "muzzy",
"copay", "cabby", "gauzy", "booby", "boomy", "mammy", "boody", "pommy", "zappy", "yummy",
"wacky", "jimmy", "hubby", "bubby", "paddy", "pappy", "goofy", "cubby", "goody", "juicy",
"hammy", "daffy", "kicky", "zippy", "pawky", "biffy", "hippy", "baggy", "howdy", "cuddy",
"daddy", "dowdy", "dippy", "happy", "huffy", "dummy", "foggy",
]
SPORTSDAY_DF = pd.read_csv(os.path.join(THIS_DIR, "sportsday.csv"))
@click.command()
@click.argument('notebook_file', type=click.Path(exists=True, dir_okay=False))
@click.option(
'--export-json', type=click.Path(dir_okay=False, writable=True),
help="Path to save grading result as a JSON file",
)
@click.option(
'--force-grade-all', is_flag=True,
help="Turn this option on to keep grading even after encountering the first mistake in each test group",
)
@click.option('--always-zero-exit', is_flag=True, help="Always returns exit status 0 even mistakes were found")
@click.option('-v', '--verbose', count=True, help="Produce more detailed message (can be stacked)")
@click.option('-q', '--quiet', count=True, help="Produce less detailed message (can be stacked)")
@click.option('--width', type=int, callback=validate_pos, help="Width of terminal (default: auto detect)")
@click.option('--height', type=int, callback=validate_pos, help="Height of terminal (default: auto detect)")
@click.option('--force-color/--no-force-color', default=None, help="Display color on terminal (default: auto detect)")
def program(
notebook_file: str,
export_json: str = None,
force_grade_all: bool = False,
always_zero_exit: bool = False,
verbose: int = 0,
quiet: int = 0,
width: int = None,
height: int = None,
force_color: bool = None,
):
"""Grades the exam notebook."""
ctx = ApplicationContext(
force_grade_all=force_grade_all,
always_zero_exit=always_zero_exit,
verbose=verbose,
quiet=quiet,
width=width,
height=height,
force_color=force_color,
)
# Setup rich console object for pretty terminal printing
ctx.console.print(rich_time(), "Grading script started")
# Load the exam notebook from the specified path as a Python module object
nb_loader = NotebookLoader(console=ctx.console, show_skip_cells=ctx.show_skip_cells)
module_name = 'exam'
try:
mod = nb_loader.load_module(module_name, notebook_file)
except Exception as exc:
ctx.console.print_exception()
if export_json:
with open(export_json, "w") as f:
json.dump(TEST_SUITE.do_skip().serialize(), f, ensure_ascii=False, indent=2)
raise SystemExit(1) from exc
# Execute the entire test suite and print the score summary
result = TEST_SUITE.execute(ctx, mod)
ctx.console.print(rich_time(), result.rich_score())
# Export the result into JSON file if option is set
if export_json:
with open(export_json, "w") as f:
json.dump(result.serialize(), f, ensure_ascii=False, indent=2)
ctx.console.print(rich_time(), "Result written to", repr(export_json))
raise SystemExit(0 if always_zero_exit or result.is_perfect else 1)
@TestScenario.create(ModuleItem('count_even'), Param(), Param())
def test_count_even(func: Callable[[list[int]], int], values: list[int], expected: int) -> None:
result = func(values)
unit_testing.assertEqual(result, expected)
@TestScenario.create(ModuleItem('cone_surface_area'), Param(), Param(), Param())
def test_cone_surface_area(
func: Callable[[float, float], float],
radius: float,
height: float,
expected: float,
) -> None:
result = func(radius, height)
unit_testing.assertEqual(result, approx(expected))
@TestScenario.create(ModuleItem('filter_matching'), Param(), Param(), Param(), Param())
def test_filter_matching(
func: Callable[[list[str], str, int], list[str]],
wordbank: list[str],
target: str,
position: int,
expected: list[str],
) -> None:
result = func(wordbank, target, position)
unit_testing.assertEqual(result, expected)
@TestScenario.create(ModuleItem('filter_absence'), Param(), Param(), Param())
def test_filter_absence(
func: Callable[[list[str], str], list[str]],
wordbank: list[str],
unwanted: str,
expected: list[str],
) -> None:
result = func(wordbank, unwanted)
unit_testing.assertEqual(result, expected)
@TestScenario.create(ModuleItem('filter_misplaced'), Param(), Param(), Param(), Param())
def test_filter_misplaced(
func: Callable[[list[str], str, list[int]], list[str]],
wordbank: list[str],
target: str,
unwanted_positions: list[int],
expected: list[str],
) -> None:
result = func(wordbank, target, unwanted_positions)
unit_testing.assertEqual(result, expected)
@TestScenario.create(ModuleItem('all_prefixes'), Param(), Param())
def test_all_prefixes(func: Callable[[str], list[str]], s: str, expected: list[str]) -> None:
result = func(s)
unit_testing.assertEqual(result, expected)
@TestScenario.create(ModuleItem('longest_common_prefix'), Param(), Param(), Param())
def test_longest_common_prefix(func: Callable[[str, str], str], s1: str, s2: str, expected: str) -> None:
result = func(s1, s2)
unit_testing.assertEqual(result, expected)
@TestScenario.create(ModuleItem('appear_once'), Param(), Param())
def test_appear_once(func: Callable[[list[int]], set[int]], values: list[int], expected: set[int]) -> None:
result = func(values)
unit_testing.assertEqual(result, expected)
@TestScenario.create(ModuleItem('create_enumerate_array'), Param(), Param(), Param())
def test_create_enumerate_array(
func: Callable[[int, int], np.ndarray],
nrows: int,
ncols: int,
expected: np.ndarray,
) -> None:
result = func(nrows, ncols)
np.testing.assert_array_equal(result, expected)
@TestScenario.create(ModuleItem('rotate_point'), Param(), Param(), Param())
def test_rotate_point(
func: Callable[[np.ndarray, float], np.ndarray],
point: np.ndarray,
angle: float,
expected: np.ndarray,
) -> None:
result = func(point, angle)
np.testing.assert_allclose(result, expected, atol=1e-9)
@TestScenario.create(ModuleItem('remove_neg_sum_cols'), Param(), Param())
def test_remove_neg_sum_cols(
func: Callable[[np.ndarray], np.ndarray],
array: np.ndarray,
expected: np.ndarray,
) -> None:
result = func(array)
np.testing.assert_equal(result, expected)
@TestScenario.create(ModuleItem('subtract_by_row_min'), Param(), Param())
def test_subtract_by_row_min(
func: Callable[[np.ndarray], np.ndarray],
array: np.ndarray,
expected: np.ndarray,
) -> None:
result = func(array)
np.testing.assert_equal(result, expected)
@TestScenario.create(ModuleItem('common_last_name'), Param(), Param())
def test_common_last_name(
func: Callable[[pd.DataFrame], str],
df: pd.DataFrame,
expected: str,
) -> None:
result = func(df)
np.testing.assert_equal(result, expected)
@TestScenario.create(ModuleItem('fast_runners'), Param(), Param(), Param(), Param())
def test_fast_runners(
func: Callable[[pd.DataFrame, str, float], pd.DataFrame],
df: pd.DataFrame,
team_color: str,
max_running_time: float,
expected: set[tuple[str, str]],
) -> None:
result = func(df, team_color, max_running_time)
pd.testing.assert_index_equal(result.columns, SPORTSDAY_DF.columns)
unit_testing.assertEqual(
set(result[['first_name', 'last_name']].itertuples(index=False, name=None)),
expected,
)
@TestScenario.create(ModuleItem('best_bowling_and_minigolf'), Param(), Param(), Param())
def test_best_bowling_and_minigolf(
func: Callable[[pd.DataFrame, int], tuple[float, float]],
df: pd.DataFrame,
class_no: int,
expected: tuple[float, float],
) -> None:
result = func(df, class_no)
unit_testing.assertEqual(result, expected)
@TestScenario.create(ModuleItem('highjump_averages'), Param(), Param())
def test_highjump_averages(
func: Callable[[pd.DataFrame], pd.Series],
df: pd.DataFrame,
expected: dict[str, float],
) -> None:
result = func(df)
unit_testing.assertEqual(result.to_dict(), approx(expected))
TEST_SUITE = TestSuite([
TestGroup("0. Count Even", 10, [
test_count_even.data([1, 2, 3, 4, 5], 2),
test_count_even.data([1, 3, 5, 7], 0),
test_count_even.data([2, 4, 8, 16, 32], 5),
test_count_even.data([], 0),
]),
TestGroup("1. Cone Surface Area", 10, [
test_cone_surface_area.data(2, 3, 35.22071741263713),
test_cone_surface_area.data(2, 0, 8 * math.pi),
test_cone_surface_area.data(0, 3, 0),
test_cone_surface_area.data(3, 4, 24 * math.pi),
]),
TestGroup("2.1 Wordle Matching", 10, [
test_filter_matching.data(["hippo", "zebra", "panda", "koala"], "a", 5, ["zebra", "panda", "koala"]),
test_filter_matching.data(["hippo", "zebra", "panda", "koala"], "e", 2, ["zebra"]),
test_filter_matching.data(["hippo", "zebra", "panda", "koala"], "x", 1, []),
test_filter_matching.data(FUNNY_WORDS, "m", 1, [
"miffy", "muddy", "moody", "muggy", "mummy", "mommy", "middy", "muzzy", "mammy",
]),
test_filter_matching.data(FUNNY_WORDS, "m", 2, []),
test_filter_matching.data(FUNNY_WORDS, "i", 3, ["juicy"]),
test_filter_matching.data(FUNNY_WORDS, "e", 4, []),
test_filter_matching.data(FUNNY_WORDS, "y", 5, FUNNY_WORDS),
test_filter_matching.data(FUNNY_WORDS, "k", 4, [
"cocky", "yucky", "choky", "dicky", "hooky", "gawky", "ducky", "cooky", "kooky", "picky",
"wacky", "kicky", "pawky",
]),
test_filter_matching.data([], "x", 2, []),
]),
TestGroup("2.2 Wordle Absence", 10, [
test_filter_absence.data(["hippo", "zebra", "panda", "koala"], "a", ["hippo"]),
test_filter_absence.data(["zebra", "panda", "koala"], "a", []),
test_filter_absence.data(["hippo", "zebra", "panda", "koala"], "x", ["hippo", "zebra", "panda", "koala"]),
test_filter_absence.data(FUNNY_WORDS, "m", [
"poboy", "dobby", "cuppy", "hobby", "pudgy", "cocky", "bawdy", "jazzy", "caddy", "jaggy",
"duchy", "poppy", "yucky", "biddy", "choky", "fuzzy", "buggy", "byway", "gaudy", "dizzy",
"gabby", "dicky", "doggy", "wiggy", "hooky", "kiddy", "waddy", "guppy", "woody", "doozy",
"giddy", "piggy", "divvy", "gawky", "bobby", "woozy", "ducky", "buddy", "cooky", "dodgy",
"kooky", "jiffy", "picky", "puppy", "copay", "cabby", "gauzy", "booby", "boody", "zappy",
"wacky", "hubby", "bubby", "paddy", "pappy", "goofy", "cubby", "goody", "juicy", "daffy",
"kicky", "zippy", "pawky", "biffy", "hippy", "baggy", "howdy", "cuddy", "daddy", "dowdy",
"dippy", "happy", "huffy", "foggy",
]),
test_filter_absence.data(FUNNY_WORDS, "u", [
"poboy", "dobby", "hobby", "cocky", "bawdy", "miffy", "foamy", "jazzy", "caddy", "jaggy",
"poppy", "biddy", "choky", "byway", "dizzy", "moody", "gabby", "dicky", "doggy", "wiggy",
"hooky", "kiddy", "waddy", "woody", "doozy", "giddy", "gamay", "piggy", "divvy", "gawky",
"bobby", "woozy", "cooky", "mommy", "dodgy", "middy", "kooky", "jiffy", "picky", "comfy",
"copay", "cabby", "booby", "boomy", "mammy", "boody", "pommy", "zappy", "wacky", "jimmy",
"paddy", "pappy", "goofy", "goody", "hammy", "daffy", "kicky", "zippy", "pawky", "biffy",
"hippy", "baggy", "howdy", "daddy", "dowdy", "dippy", "happy", "foggy",
]),
test_filter_absence.data(FUNNY_WORDS, "y", []),
test_filter_absence.data(FUNNY_WORDS, "r", FUNNY_WORDS),
test_filter_absence.data([], "j", []),
]),
TestGroup("2.3 Wordle Misplaced", 10, [
test_filter_misplaced.data(["hippo", "zebra", "panda", "koala"], "a", [2, 3], ["zebra"]),
test_filter_misplaced.data(["hippo", "zebra", "panda", "koala"], "o", [1, 4], ["hippo", "koala"]),
test_filter_misplaced.data(["hippo", "zebra", "panda", "koala"], "x", [1, 2, 5], []),
test_filter_misplaced.data(FUNNY_WORDS, "m", [1, 4], [
"jumpy", "bumpy", "dumpy", "gamay", "humpy", "comfy",
]),
test_filter_misplaced.data(FUNNY_WORDS, "u", [2, 3], []),
test_filter_misplaced.data(FUNNY_WORDS, "f", [], [
"miffy", "foamy", "fuzzy", "jiffy", "comfy", "goofy", "daffy", "biffy", "huffy", "foggy",
]),
test_filter_misplaced.data(FUNNY_WORDS, "y", [3], FUNNY_WORDS),
test_filter_misplaced.data(FUNNY_WORDS, "o", [1, 2, 3, 4, 5], []),
test_filter_misplaced.data([], "x", [3, 5], []),
]),
TestGroup("3.1 All Prefixes", 10, [
test_all_prefixes.data("APPLES", ["", "A", "AP", "APP", "APPL", "APPLE", "APPLES"]),
test_all_prefixes.data("Hello, World!", [
"",
"H",
"He",
"Hel",
"Hell",
"Hello",
"Hello,",
"Hello, ",
"Hello, W",
"Hello, Wo",
"Hello, Wor",
"Hello, Worl",
"Hello, World",
"Hello, World!",
]),
test_all_prefixes.data("👨🦱", ["", "👨", "👨\u200d", "👨\u200d🦱"]),
test_all_prefixes.data("", [""]),
]),
TestGroup("3.2 Longest Common Prefix", 10, [
test_longest_common_prefix.data("dolphin", "dog", "do"),
test_longest_common_prefix.data("dog", "cat", ""),
test_longest_common_prefix.data("anteater", "ant", "ant"),
test_longest_common_prefix.data("ant", "anteater", "ant"),
test_longest_common_prefix.data("", "", ""),
]),
TestGroup("4. Appear Once", 10, [
test_appear_once.data([1, 2, 3, 4, 5, 3, 6, 4, 8], {1, 2, 5, 6, 8}),
test_appear_once.data([1, 3, 5, 7, 9], {1, 3, 5, 7, 9}),
test_appear_once.data([1, 1, 2, 2, 3, 3, 3], set()),
test_appear_once.data([], set()),
]),
TestGroup("5. Column Major Enum Array", 10, [
test_create_enumerate_array.data(2, 3, np.array([
[1, 3, 5],
[2, 4, 6],
])),
test_create_enumerate_array.data(3, 2, np.array([
[1, 4],
[2, 5],
[3, 6],
])),
test_create_enumerate_array.data(1, 1, np.array([[1]])),
]),
TestGroup("6. Rotate Point", 10, [
test_rotate_point.data(np.array([1, 2]), 90, np.array([-2, 1])),
test_rotate_point.data(np.array([-4, 1]), -45, np.array([-2.121320343559643, 3.5355339059327373])),
test_rotate_point.data(np.array([7.25, -4.75]), 108, np.array([2.2771452431836114, 8.362990466420865])),
test_rotate_point.data(np.array([-12, -16]), -143.13010235415598, np.array([0, 20])),
test_rotate_point.data(np.array([3, -6]), 0, np.array([3, -6])),
test_rotate_point.data(np.array([-8, 4]), 360, np.array([-8, 4])),
test_rotate_point.data(np.array([0, 0]), 17.29, np.array([0, 0])),
]),
TestGroup("7. Remove Negative-Sum Columns", 10, [
test_remove_neg_sum_cols.data(
np.array([
[3, 2, -6, -3, -6],
[-3, -5, 1, 3, -5],
[3, 2, 2, -1, 2],
[-3, 1, -5, -1, -3],
]),
np.array([
[3, 2],
[-3, -5],
[3, 2],
[-3, 1],
]),
),
test_remove_neg_sum_cols.data(
np.array([
[-5, -12, -9, -6],
[-15, 2, 2, -9],
[-11, -7, -15, 4],
]),
np.array([[], [], []]),
),
test_remove_neg_sum_cols.data(
np.array([
[0, 3, 3, 1],
[4, 0, 3, -2],
[-1, 2, 4, 3],
]),
np.array([
[0, 3, 3, 1],
[4, 0, 3, -2],
[-1, 2, 4, 3],
]),
),
test_remove_neg_sum_cols.data(
np.array([
[7, 2, -6],
[3, -8, -4],
[-4, -9, -4],
[-6, 13, 15],
]),
np.array([
[7, -6],
[3, -4],
[-4, -4],
[-6, 15],
]),
),
]),
TestGroup("8. Subtract By Row Minimum", 10, [
test_subtract_by_row_min.data(
np.array([
[48, 54, 79, -22, -23],
[-72, -39, 45, -21, -53],
[100, 62, -74, 0, 95],
[99, -2, 58, 12, 60],
]),
np.array([
[71, 77, 102, 1, 0],
[0, 33, 117, 51, 19],
[174, 136, 0, 74, 169],
[101, 0, 60, 14, 62],
]),
),
test_subtract_by_row_min.data(
np.array([
[16, 70, 24, -9],
[90, 76, 49, 91],
[65, 72, 80, 55],
]),
np.array([
[25, 79, 33, 0],
[41, 27, 0, 42],
[10, 17, 25, 0],
]),
),
test_subtract_by_row_min.data(
np.array([
[241, 319, 241],
[254, 266, 118],
]),
np.array([
[0, 78, 0],
[136, 148, 0],
]),
),
]),
TestGroup("9. Common Last Name", 10, [
test_common_last_name.data(SPORTSDAY_DF, 8),
test_common_last_name.data(SPORTSDAY_DF[SPORTSDAY_DF.team_color == 'salmon_red'], 4),
test_common_last_name.data(SPORTSDAY_DF[SPORTSDAY_DF['class'] == 7], 3),
]),
TestGroup("10. Fast Runners", 10, [
test_fast_runners.data(SPORTSDAY_DF, 'salmon_red', 12.0, {
('Angela', 'Williams'),
('Emily', 'Flores'),
('Gary', 'Martinez'),
('Jason', 'Scott'),
('Justin', 'Lopez'),
}),
test_fast_runners.data(SPORTSDAY_DF, 'caterpillar_green', 11.5, {
('Daniel', 'Rodriguez'),
('Kathleen', 'Edwards'),
('Linda', 'Turner'),
}),
test_fast_runners.data(SPORTSDAY_DF, 'whale_blue', 11.0, set()),
]),
TestGroup("11. Best Bowling and Minigolf", 10, [
test_best_bowling_and_minigolf.data(SPORTSDAY_DF, 8, (291, 18)),
test_best_bowling_and_minigolf.data(SPORTSDAY_DF, 10, (284, 21)),
test_best_bowling_and_minigolf.data(SPORTSDAY_DF, 12, (300, 18)),
]),
TestGroup("12. High Jump Averages", 10, [
test_highjump_averages.data(SPORTSDAY_DF, {
'caterpillar_green': 93.21265306122449,
'duckling_yellow': 98.88854166666665,
'salmon_red': 89.11403846153847,
'whale_blue': 93.18333333333334,
}),
test_highjump_averages.data(SPORTSDAY_DF[SPORTSDAY_DF['class'] == 9], {
'caterpillar_green': 103.64555555555555,
'duckling_yellow': 100.98625,
'salmon_red': 83.57,
'whale_blue': 90.29,
}),
test_highjump_averages.data(SPORTSDAY_DF[SPORTSDAY_DF['class'].isin([7, 11])], {
'caterpillar_green': 87.44583333333333,
'duckling_yellow': 101.9042105263158,
'salmon_red': 90.19999999999999,
'whale_blue': 96.38307692307693,
}),
]),
])
if __name__ == '__main__':
program()